Method and system for achieving high availability of service under high-load scene in distributed system

ABSTRACT

Provided are a method and system for achieving high availability of service under a high-load scene in a distributed system. The method includes constructing a node selection model at a master node of a distributed cluster; constructing a request selection model in each slave node; wherein the request selection model includes weights of designated requests and trade-off parameters set for requests each having a weight greater than a set value; when the distributed system enters the high-load scene, reading, by the master node, the node selection model, and sequentially selecting the served slave nodes according to a time slice round robin policy; and reading, by each slave node, the request selection model, and sequentially returning data of each request according to the trade-off parameters of each request.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a 35 U.S.C. 371 National Stage Patent Application ofInternational Application No. PCT/CN2021/076981, filed Feb. 20, 2021,which claims priority to Chinese application 202010741416.4, filed Jul.29, 2020, each of which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The present disclosure relates to the technical field of highavailability of service of a distributed storage system, and inparticular, relates to a method and system for achieving highavailability of service under a high-load scene in a distributed system.

BACKGROUND

In a distributed storage system, under a high-load pressure, how toensure the system not to undergo crashes or downtime is a key highavailability standard of the distributed storage system. Most solutionsfor solving high-load are to perform shunting in advance so as toalleviate the pressure of a certain server; however, in practicalscenes, high-load is an inevitable situation. How to ensure that userservice is still available under a high-load situation is a problem thatmust be solved in a distributed storage system.

SUMMARY

Aiming at the problem that must be solved in a distributed storagesystem, i.e. how to ensure that user service is still available under ahigh-load situation, some embodiments of the present disclosure providea method and system for achieving high availability of service under ahigh-load scene in a distributed system. The technical solutions of someembodiments of the present disclosure are provided,

In one aspect, the technical solutions of some embodiments of thepresent disclosure provide a method for achieving high availability ofservice under a high-load scene in a distributed system, including thefollowing steps:

constructing a node selection model at a master node of a distributedcluster; wherein the node selection model is used for the master node toselect, from the node selection model, served slave nodes and thesequence at which the slave nodes are served;

constructing a request selection model in each slave node; wherein therequest selection model includes weights of designated requests andtrade-off parameters set for requests each having a weight greater thana set value; the request selection model is used for each slave node toselect, from the request selection model, responded requests and thesequence of the responded requests;

when the distributed system enters the high-load scene, reading, by themaster node, the node selection model, and sequentially selecting theserved slave nodes according to a time slice round robin policy; whereinthe master node uses time slice round robin to ensure that each node canbe served; and

reading, by each slave node, the request selection model, andsequentially returning data of each request according to the trade-offparameters of each request; wherein the policy of trade-off parametersof requests in each slave node ensures that each slave node does not getdown, and reduces the load of the whole system to the greatest extent,thereby achieving the availability of the whole cluster.

In an embodiment, the step of constructing a node selection model at amaster node of a distributed cluster includes:

providing a monitor on the master node to monitor the source of eachrequest, the data size of each request, and the total time of eachrequest;

when a load of the system is normal, periodically and statisticallyanalyzing the number of times of requests, the type of the requests, andthe data size and average time of the requests of each slave node, andcalculating the average number of times of requests, the average datasize of the requests and the average total time of the requests of eachhour of different slave nodes; and

sorting corresponding slave nodes according to the number of times ofrequests to form the node selection model. The construction of the nodeselection model is achieved on the master node. By monitoring the sourceof each request, the data size of each request and the total time ofeach request on the master node, the average number of times ofrequests, the average data size of the requests and the average totaltime of the requests during each hour are calculated, a node selectionmodel based on the number of times of requests and the average data sizeof the requests is obtained.

In an embodiment, the step of sorting corresponding slave nodesaccording to the number of times of requests to form the node selectionmodel further includes:

sorting the corresponding slave nodes according to the number of timesof requests, and when the number of times of requests is the same,sorting the corresponding slave nodes according to the average data sizeof the requests to form the node selection model. In an embodiment, theslave nodes are served according to a descending order of the number oftimes of requests of the slave nodes; and for slave nodes having thesame number of times of requests, the slave nodes are served accordingto an ascending order of the average data size of requests of the slavenodes.

In an embodiment, the step of constructing a request selection model ineach slave node includes:

setting weights for different requests according to service importancedegrees thereof;

setting trade-off parameters for each request with a weight greater thanor equal to a set threshold; and

sorting corresponding requests according to the sizes of the weights toform the request selection model. The construction of the requestselection model is on each slave node. Weight values of requests are setfor requests of each slave node, and the greater the weight value is,the more important the basic service processing of the request is to thenode. For a request with a weight greater than a set threshold, atrade-off parameter policy is set, the parameter policy specifying theminimum response parameter satisfaction degree available to the request.

In an embodiment, before the step of when the distributed system entersthe high-load scene, reading, by the master node, the node selectionmodel, and sequentially selecting the served slave nodes according to atime slice round robin policy, the method includes:

when the distributed system enters the high-load scene, stopping, by themaster node, monitoring and calculation on the requests of each slavenode. In a high-load scene, monitoring of the master node can be turnedoff at any time, so as to reduce the coupling degree of the wholesystem, and reduce the loss of the cluster itself caused by the wholetechnology model.

In an embodiment, in the step of reading, by the master node, the nodeselection model, and sequentially selecting the served slave nodesaccording to a time slice round robin policy, the time slice served byeach salve node=(the number of times of requests/the number of totalnodes)×the average total time of the requests×1/N, where N is an integergreater than 3.

On the other aspect, the technical solutions of some embodiments of thepresent disclosure provide a system for achieving high availability ofservice under a high-load scene in a distributed system, including amaster node, several slave nodes, a node selection model constructionmodule and request selection model construction modules;

the node selection model construction module is configured to constructa node selection model at a master node of a distributed cluster; thenode selection model is used for the master node to select, from thenode selection model, served slave nodes and the sequence at which theslave nodes are served;

each request selection model construction module is configured toconstruct a request selection model in each slave node; wherein therequest selection model includes weights of designated requests andtrade-off parameters set for requests each having a weight greater thana set value; and each request selection model is used for each slavenode to select, from the request selection model, responded requests andthe sequence of the responded requests;

the master node is configured to read the node selection model when thedistributed system enters the high-load scene, and sequentially selectthe served slave nodes according to a time slice round robin policy; and

each slave node is configured to read the request selection model, andsequentially return data of each request according to the trade-offparameters of each request.

In an embodiment, the node selection model construction module includesa calculation processor, a node selection model generator and a monitorwhich is provided on the master node;

the monitor is configured to monitor the source of each request, thedata size of each request, and the total time of each request;

the calculation processor is configured to when a load of the system isnormal, periodically and statistically analyze the number of times ofrequests, the type of the requests, and the data size and average timeof the requests of each slave node, and calculate the average number oftimes of requests, the average data size of the requests and the averagetotal time of the requests of each hour of different slave nodes; and

the node selection model generator is configured to sort correspondingslave nodes according to the number of times of requests to form thenode selection model.

In an embodiment, the node selection model generator is specificallyconfigured to sort the corresponding slave nodes according to the numberof times of requests, and when the number of times of requests is thesame, sort the corresponding slave nodes according to the average datasize of the requests to form the node selection model.

In an embodiment, each request selection model construction moduleincludes a weight setter, a trade-off parameter setter, and a requestselection model generator;

the weight setter is configured to set weights for different requestsaccording to service importance degrees thereof;

the trade-off parameter setter is configured to set trade-off parametersfor each request with a weight greater than or equal to a set threshold;and

the request selection model generator is configured to sortcorresponding requests according to the sizes of the weights to form therequest selection model.

It can be determined from the technical solutions that the presentdisclosure has the following advantages: the construction of models aredistributed on the master node and slave nodes, and in a high-loadscene, monitoring of the master node can be turned off at any time, soas to reduce the coupling degree of the whole system, and reduce theloss of the cluster itself caused by the whole technology model. Themodel of the master node is calculated according to actual requests, andthe models of the slave nodes are set according to specific servicescenes, and thus the construction and selection of the whole technologymodel is more suitable for actual storage services, and data obtained ismore accurate. The master node uses time slice round robin to ensurethat each node can be served, and the policy of trade-off of requests ineach slave node ensures that each slave node does not get down, andreduces the load of the whole system to the greatest extent, therebyachieving the availability of the whole cluster.

In addition, the present disclosure has a reliable design principle, asimple structure, and has a very broad application prospect.

Hence, compared with the related art, the present disclosure hasprominent substantive features and notable progress, and the beneficialeffects of implementation thereof are also obvious.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the technical solutions in the embodiments of thepresent disclosure or in the related art more clearly, hereinafter,accompanying drawings requiring to be used for describing theembodiments or the related art are introduced briefly. Obviously, for aperson of ordinary skill in the art, other accompanying drawings mayalso be obtained according to these accompanying drawings without anyinventive effort.

FIG. 1 is a schematic flowchart of a method according to someembodiments of the present disclosure.

FIG. 2 is an exemplary diagram of a node selection model according to anembodiment of the present disclosure.

FIG. 3 is an exemplary diagram of a request selection model according toan embodiment of the present disclosure.

FIG. 4 is a schematic block diagram of a system according to someembodiments of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, in order to make a person skilled in the art betterunderstand the technical solutions of some embodiments of the presentdisclosure, the technical solutions in the embodiments of the presentdisclosure are described clearly and completely with reference to thedrawings in the embodiments of the present disclosure. Obviously, theembodiments as described are only parts of embodiments of the presentdisclosure rather than all the embodiments. All other embodimentsobtained by a person of ordinary skill in the art on the basis of theembodiments of the present disclosure without any inventive effort shallall fall within the scope of protection of the present disclosure.

As shown in FIG. 1 , embodiments of the present disclosure provide amethod for achieving high availability of service under a high-loadscene in a distributed system, including the following steps:

S1: constructing a node selection model at a master node of adistributed cluster; wherein the node selection model is used for themaster node to select, from the node selection model, served slave nodesand the sequence at which the slave nodes are served; and the nodeselection model is constructed in the master node, to obtain nodeselection basis and a duration of each node being each served.

S2: constructing a request selection model in each slave node; whereinthe request selection model includes weights of designated requests andtrade-off parameters set for requests each having a weight greater thana set value; and the request selection model is used for each slave nodeto select, from the request selection model, responded requests and thesequence of the responded requests; the request selection model isconstructed in each slave node, so as to obtain the priority of eachrequest being responded to and the minimum satisfaction degree of thedata size returned in each response.

S3: when the distributed system enters the high-load scene, reading, bythe master node, the node selection model, and sequentially selectingthe served slave nodes according to a time slice round robin policy;wherein the master node uses time slice round robin to ensure that eachnode can be served; and when the distributed system enters the high-loadscene, first according to the node selection model in the master node,the served slave nodes are selected according to the sequence in themodel and the time slice round robin policy, and the round robin time ofeach time slice is calculated according to the node selection model.

S4: reading, by each slave node, the request selection model, andsequentially returning data of each request according to the trade-offparameters of each request; wherein the policy of trade-off parametersof requests in each slave node ensures that each slave node does not getdown, and reduces the load of the whole system to the greatest extent,and then according to each slave node, the sequence in a request modelqueue, and the trade-off policy, data of each request is returned,thereby ensuring high availability of the whole service.

In some embodiments, the step of constructing a node selection model ata master node of a distributed cluster includes:

providing a monitor on the master node to monitor the source of eachrequest, the data size of each request, and the total time of eachrequest;

when a load of the system is normal, periodically and statisticallyanalyzing the number of times of requests, the type of the requests, andthe data size and average time of the requests of each slave node, andcalculating the average number of times of requests, the average datasize of the requests and the average total time of the requests of eachhour of different slave nodes; and

sorting corresponding slave nodes according to the number of times ofrequests to form the node selection model. The construction of the nodeselection model is achieved on the master node. By monitoring the sourceof each request, the data size of each request and the total time ofeach request on the master node, the average number of times ofrequests, the average data size of the requests and the average totaltime of the requests during each hour are calculated, a node selectionmodel based on the number of times of requests and the average data sizeof the requests is obtained.

First, the master node serves as a monitoring point, records andcaptures all requests processed by the master node; calculates thesources of requests thereof, the data size of the requests and the totaltime of the requests; and then constructs a reference request modelunder different slave nodes, different request types and differentreturned data sizes by taking the sources of the requests and the sizeof data returned by the requests as division basis.

In some embodiments, the step of sorting corresponding slave nodesaccording to the number of times of requests to form the node selectionmodel further includes:

sorting the corresponding slave nodes according to the number of timesof requests, and if the number of times of requests is the same, sortingthe corresponding slave nodes according to the average data size of therequests to form the node selection model. In an embodiment, the slavenodes are served according to a descending order of the number of timesof requests of the slave nodes; and for slave nodes having the samenumber of times of requests, the slave nodes are served according to anascending order of the average data size of requests of the slave nodes.

In some embodiments, the step of constructing a request selection modelin each slave node includes:

setting weights for different requests according to service importancedegrees thereof; here, the weights are set as 10-0;

setting trade-off parameters for each request with a weight greater thanor equal to a set threshold; then, with regard to each request with aweight of 5 or 5 and more, setting a trade-off parameter set, in whichthe trade-off parameter policy only functions under a high-load scene,and the parameter set specifies the minimum satisfaction degree policyof the master node to the request under the high-load scene; and

sorting corresponding requests according to the sizes of the weights toform the request selection model. The construction of the requestselection model is on each slave node. In slave nodes of the distributedsystem, for each request sent to the master node, the weight degrees ofrequests of each slave node are set according to actual service thereof.The higher the weight is, the more important the request is to serviceavailability. In a high-load, it needs to continuously ensure that arequest with a lower weight has lower importance degree, and can beresponded to in a delayed manner in a high-load environment. Moreover,for each request, according to specific service scene thereof, a returntrade-off parameter policy is set, in which the trade-off parameterpolicy is a minimum parameter satisfaction degree for normal serviceoperation.

In some embodiments, before the step of when the distributed systementers the high-load scene, reading, by the master node, the nodeselection model, and sequentially selecting the served slave nodesaccording to a time slice round robin policy, the method includes:

when the distributed system enters the high-load scene, the master nodestop monitoring and calculation on the requests of each slave node. In ahigh-load scene, monitoring of the master node can be turned off at anytime, so as to reduce the coupling degree of the whole system, andreduce the loss of the cluster itself caused by the whole technologymodel.

In some embodiments, when the distributed cluster enters the high-loadscene, the master node first selects, according to the node selectionmodel, the sequence at which nodes are served, the specific services arebased on a time slice round robin policy, and the time served by eachnode is calculated according to formula: (the number of times ofrequests/the number of total nodes)×the average total time of therequests×1/5. For slave nodes which are selected to be served, firstaccording to weights of requests, requests preferentially responded toare selected, and then according to the trade-off parameter policy,parameters of the minimum data size satisfied by the service arereturned. In this way, normal operation of each node of the distributedcluster under a high-load environment is achieved.

As shown in FIG. 2 , the node selection model on the master nodeincludes the number of times of requests, the average data size ofrequests of each node, and the average time of the requests per hour.Moreover, the arrangement sequence of slave nodes is first according tothe number of times of requests, and slave nodes having higher number oftimes of requests are arranged at relatively front positions; and if thenumber of times of requests is the same, according to the average datasize of the requests, slave nodes having lower average data size arearranged at the front positions. For the request selection model on eachslave node, as shown in FIG. 3 , the weight of each request isdesignated, and for requests with weights greater than 5, trade-offparameters and full parameters of the requests are set. The slave nodesperform requests according to first come first served policy in a normalsituation of the cluster; however, in a high-load cluster, requests tobe sent will be sorted according to a priority level of weights; andrequests with weights less than 5 are internally ignored, and for thesame priority level, the slave nodes are sorted according to a firstcome first served principle.

When the distributed cluster is in the high-load scene, the master nodefirst disables the monitoring and calculation on service object modelsof the master node, to reduce service consumption. Then the nodeselection model is read, and the arrangement nodes are served insequence according to a time slice round robin method, wherein the timeof a time slice served by each node is (the number of times ofrequests/the number of total nodes)×the average total time of therequests×1/5. For each node, a request queue is read, and responding ismade according to specified contents of the trade-off parameters ofparameters of each request. In this way, high availability of service ofthe whole cluster is ensured.

As shown in FIG. 4 , embodiments of the present disclosure provide asystem for achieving high availability of service under a high-loadscene in a distributed system, including a master node, several slavenodes, a node selection model construction module and request selectionmodel construction modules;

the node selection model construction module is configured to constructa node selection model at a master node of a distributed cluster; thenode selection model is used for the master node to select, from thenode selection model, served slave nodes and the sequence at which theslave nodes are served;

each request selection model construction module is configured toconstruct a request selection model in each slave node; wherein therequest selection model includes weights of designated requests andtrade-off parameters set for requests each having a weight greater thana set value; and each request selection model is used for each slavenode to select responded requests from the request selection model, andselect the sequence of the responded requests according to the sizes ofthe weights;

the master node is configured to read the node selection model when thedistributed system enters the high-load scene, and sequentially selectthe served slave nodes according to a time slice round robin policy; and

each slave node is configured to read the request selection model, andsequentially return data of each request according to the trade-offparameters of each request.

In some embodiments, the node selection model construction moduleincludes a calculation processor, a node selection model generator and amonitor which is provided on the master node;

the monitor is configured to monitor the source of each request, thedata size of each request, and the total time of each request;

the calculation processor is configured to when a load of the system isnormal, periodically and statistically analyze the number of times ofrequests, the type of the requests, and the data size and average timeof the requests of each slave node, and calculate the average number oftimes of requests, the average data size of the requests and the averagetotal time of the requests of each hour of different slave nodes; and

the node selection model generator is configured to sort correspondingslave nodes according to the number of times of requests to form thenode selection model.

In some embodiments, the node selection model generator is specificallyconfigured to sort the corresponding slave nodes according to the numberof times of requests, and if the number of times of requests is thesame, sort the corresponding slave nodes according to the average datasize of the requests to form the node selection model.

In some embodiments, each request selection model construction moduleincludes a weight setter, a trade-off parameter setter, and a requestselection model generator;

the weight setter is configured to set weights for different requestsaccording to service importance degrees thereof;

the trade-off parameter setter is configured to set trade-off parametersfor each request with a weight greater than or equal to a set threshold;and

the request selection model generator is configured to sortcorresponding requests according to the sizes of the weights to form therequest selection model.

The node selection model is used as a reference basis for the masternode to serve each slave node under a high-load scene. The requestselection model is used as a basis for each request in each slave nodeto be served under a high-load scene. Under a high-load scene, high-loadcluster service technology selects served nodes with reference to thenode selection model; and for each served slave node, a request for aspecific service is selected by the request thereof responding to themodel. In this way, high availability of the whole system is achieved.

The construction of the node selection model is achieved on the masternode. By monitoring the source of each request, the data size of eachrequest and the total time of each request on the master node, a nodeselection model based on the number of times of requests and the datasize of the requests is obtained. The construction of the requestselection model is on each slave node. Weight values of requests are setfor requests of each slave node, and the greater the weight value is,the more important the basic service processing of the request is to thenode. For a request with a weight greater than 5, a trade-off parameterpolicy is set, the parameter policy specifying the minimum responseparameter satisfaction degree available to the request.

Although the present disclosure is described in detail with reference tothe accompanying drawings in combination with preferred embodiments, thepresent disclosure is not limited thereto. A person of ordinary skill inthe art may make various equivalent modifications or replacements to theembodiments of the present disclosure without departing from the spiritand essence of the present disclosure, and these modifications orreplacements shall fall within the scope of protection of the presentdisclosure. A person skilled in the art would have readily conceived ofvariations or replacements within the technical scope disclosed in thepresent disclosure, and the variations or replacements shall all fallwithin the scope of protection of the present disclosure. Thus, thescope of protection of the present disclosure shall be subject to thescope of protection of the claims.

What is claimed is:
 1. A method for achieving high availability ofservice under a high-load scene in a distributed system, comprising thefollowing steps: constructing a node selection model at a master node ofa distributed cluster; wherein the node selection model is used for themaster node to select, from the node selection model, served slave nodesand the sequence at which the slave nodes are served; constructing arequest selection model in the slave node; wherein the request selectionmodel comprises weights of designated requests and trade-off parametersset for requests each having a weight greater than a set value; therequest selection model is used for each slave node to select, from therequest selection model, responded requests and the sequence of theresponded requests; when the distributed system enters the high-loadscene, reading, by the master node, the node selection model, andsequentially selecting the served slave nodes according to a time sliceround robin policy; and reading, by each slave node, the requestselection model, and sequentially returning data of each requestaccording to the trade-off parameters of each request.
 2. The methodaccording to claim 1, wherein the step of constructing a node selectionmodel at a master node of a distributed cluster comprises: providing amonitor on the master node to monitor the source of each request, thedata size of each request, and the total time of each request; when aload of the system is normal, periodically and statistically analyzingthe number of times of requests, the type of the requests, and the datasize and average time of the requests of each slave node, andcalculating the average number of times of requests, the average datasize of the requests and the average total time of the requests of eachhour of different slave nodes; and sorting corresponding slave nodesaccording to the number of times of requests to form the node selectionmodel.
 3. The method according to claim 2, wherein the step of sortingcorresponding slave nodes according to the number of times of requeststo form the node selection model further comprises: sorting thecorresponding slave nodes according to the number of times of requests,and when the number of times of requests is the same, sorting thecorresponding slave nodes according to the average data size of therequests to form the node selection model.
 4. The method according toclaim 1, wherein the step of constructing a request selection model ineach slave node comprises: setting weights for different requestsaccording to service importance degrees of the different requests;setting trade-off parameters for each request with a weight greater thanor equal to a set threshold; and sorting corresponding requestsaccording to the sizes of the weights to form the request selectionmodel.
 5. The method according to claim 2, wherein before the step ofwhen the distributed system enters the high-load scene, reading, by themaster node, the node selection model, and sequentially selecting theserved slave nodes according to a time slice round robin policy, themethod comprises: when the distributed system enters the high-loadscene, stopping, by the master node, monitoring and calculation on therequests of each slave node.
 6. The method according to claim 2, whereinin the step of reading, by the master node, the node selection model,and sequentially selecting the served slave nodes according to a timeslice round robin policy, the time slice served by each salve node=(thenumber of times of requests/the number of total nodes)×the average totaltime of the requests×1/N, where N is an integer greater than
 3. 7. Themethod according to claim 1, wherein the trade-off parameter representsa minimum parameter satisfaction degree for normal service operation. 8.The method according to claim 1, wherein for the same priority level,the slave nodes are sorted according to a first come first servedprinciple.
 9. The method according to claim 1, wherein for each requestsent to the master node, the weight degrees of requests of each slavenode are set according to actual service, and the higher the weight is,the more important the request is to service availability.
 10. Themethod according to claim 1, wherein the slave nodes perform requestsaccording to first come first served policy in a normal situation of thecluster, in a high-load cluster, requests to be sent be sorted accordingto a priority level of weights.
 11. A system for achieving highavailability of service under a high-load scene in a distributed system,comprising a master node, several slave nodes, a node selection modelconstruction module and request selection model construction modules;the node selection model construction module is configured to constructa node selection model at a master node of a distributed cluster; thenode selection model is used for the master node to select, from thenode selection model, served slave nodes and the sequence at which theslave nodes are served; request selection model construction module isconfigured to construct a request selection model in each slave node;wherein the request selection model comprises weights of designatedrequests and trade-off parameters set for requests each having a weightgreater than a set value; and each request selection model is used foreach slave node to select, from the request selection model, respondedrequests and the sequence of the responded requests; the master node isconfigured to read the node selection model when the distributed systementers the high-load scene, and sequentially select the served slavenodes according to a time slice round robin policy; and each slave nodeis configured to read the request selection model, and sequentiallyreturn data of each request according to the trade-off parameters ofeach request.
 12. The system according to claim 11, wherein the nodeselection model construction module comprises a calculation processor, anode selection model generator and a monitor which is provided on themaster node; the monitor is configured to monitor the source of eachrequest, the data size of each request, and the total time of eachrequest; the calculation processor is configured to when a load of thesystem is normal, periodically and statistically analyze the number oftimes of requests, the type of the requests, and the data size andaverage time of the requests of each slave node, and calculate theaverage number of times of requests, the average data size of therequests and the average total time of the requests of each hour ofdifferent slave nodes; and the node selection model generator isconfigured to sort corresponding slave nodes according to the number oftimes of requests to form the node selection model.
 13. The systemaccording to claim 12, wherein the node selection model generator isconfigured to sort the corresponding slave nodes according to the numberof times of requests, and when the number of times of requests is thesame, sort the corresponding slave nodes according to the average datasize of the requests to form the node selection model.
 14. The systemaccording to claim 11, wherein each request selection model constructionmodule comprises a weight setter, a trade-off parameter setter, and arequest selection model generator; the weight setter is configured toset weights for different requests according to service importancedegrees of the different requests; the trade-off parameter setter isconfigured to set trade-off parameters for each request with a weightgreater than or equal to a set threshold; and the request selectionmodel generator is configured to sort corresponding requests accordingto the sizes of the weights to form the request selection model.
 15. Thesystem according to claim 11, wherein wherein for each request sent tothe master node, the weight degrees of requests of each slave node areset according to actual service, and the higher the weight is, the moreimportant the request is to service availability.
 16. The systemaccording to claim 11, wherein for the same priority level, the slavenodes are sorted according to a first come first served principle. 17.The system according to claim 11, wherein the trade-off parameterrepresents a minimum parameter satisfaction degree for normal serviceoperation.
 18. The system according to claim 11, wherein the master nodeis further configured to, when the distributed system enters thehigh-load scene, stop monitoring and calculating on the requests of eachslave node.
 19. The system according to claim 11, wherein the time sliceserved by each salve node=(the number of times of requests/the number oftotal nodes)×the average total time of the requests×1/N, where N is aninteger greater than
 3. 20. The system according to claim 11, whereinfor each request sent to the master node, the weight degrees of requestsof each slave node are set according to actual service, and the higherthe weight is, the more important the request is to serviceavailability.